Question

Using the boston_housing.xls data do the ff., (you can access it online if you search in google: bostonhousing.xl) data can't be uploaded here in chegg

a.) Use the appropriate regression procedure

possible regression procedures: (coefficient of multiple determination criterion/adjusted r-square criterion/Mallow's cp statistic criterion/ prediction sum of squares criterion/ backward elimination procedure/ forward selection procedure/stepwise selection procedure)

b.) obtain a final model

Answer 1

Please run programme in python

import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
from sklearn.metrics import r2_score

from sklearn.datasets import load_boston
boston = load_boston()
boston

print(boston['DESCR'])

boston['target']

x=pd.DataFrame(boston['data'],columns=boston['feature_names'])

y=boston.target

x.isnull().sum()

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.25)

x_train.shape

x_test.shape

lr = LinearRegression()

lr.fit(x_train,y_train)

pred=lr.predict(x_test)

print('MAE', metrics.mean_absolute_error(y_test, pred))
print('MSE', metrics.mean_squared_error(y_test, pred))
print('RMSE', np.sqrt(metrics.mean_squared_error(y_test, pred)))
print('R squared error', r2_score(y_test, pred))

conclusion

  hence R Squared value is 0.73589 is slightly near to 1 hence our model fitting is good .
we can further influnce the accuracy of model using various techniqe .